Det finns en uppsjö av böcker och filmer som förklarar ridecymbalteknik och hur olika ridecymbalmönster ska spelas. Däremot saknas beskrivningar på ridecymbalteknik kopplat till handrörelser istället för rytmisering. I den här uppsatsen har ridecymbaltekniker undersökts och hur dessa kan läras ut inom ramen för kategorin tutorials på Youtube. Först studerades 18 Youtube-klipp och därefter genomfördes en kategorisering av ridecymbalteknikerna utifrån ett tekniskt perspektiv. Därefter valdes fem stycken av dessa tutorials som sedan analyserades utifrån ett multimodalt perspektiv. Studien har resulterat i att jag har fått mer kunskaper om ridecymbaltekniker och hur multimodalitet kan användas i undervisning både digitalt som analogt och betonar hur tutorials på Youtube kan ses som ett komplement till den traditionella undervisningen. Studiens fördjupning har även gett upphov till utökade tekniska och multimodala mer detaljerade kunskaper som kan kopplas till tidigare studier om exempelvis timing och multimodala aspekter som handlar om lektionsupplägg och lärarpersonligheter. / There exists a vast variety of books and movies that explain ride cymbal technique and how different patterns are supposed to be played. On the other hand there is a lack of descriptions of ride cymbal technique connected to hand movements instead of rhythmisation. In this thesis I have been investigating ride cymbal techniques and how to teach the subject. Firstly I chose to study 18 Youtube clips and subsequently five out of these which were then analysed from a multimodal perspective. The study has given me more knowledge about ride cymbal techniques and how to apply multimodality both digitally and analogically. It also shows how Youtube tutorials can complement traditional teaching approaches. The elaboration of the study has given rise to expanded more detailed knowledge related to earlier studies about for example timing and multimodal aspects regarding lesson set-up and teacher personalities.

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This dissertation consists of three essays on the empirics of firms' strategies in new industries. In the first chapter, I study the spatial mismatch between consumers and bikes in the dockless bike-sharing industry and an externality exacerbating the problem: when a consumer uses a bike for a low and inflexible price, she both displaces another consumer's usage for a potential higher-value trip, and may ride the bike to unpopular destinations. With a trip-level dataset of a bike-sharing company in Beijing, China, I develop a spatial structural model to estimate the demand for bikes with search frictions and local matchings. Compared to the scenario in which consumers always get bikes immediately, I find that local spatial mismatch between consumers and bikes reduces the total usage by 29.95%, or a net loss of 332,979 trips. Counterfactual analyses show that (1) doubling the number of bikes increases the trip volume by 28.46% while halving the number of bikes decreases the trip volume by 46.40%; (2) price-discriminating against short trips by 2% increases the total trip time by 0.22%; and (3) changing the frequency of bike reshuffling does not have a significant impact on the total usage of bikes. In the second chapter, I study how efficient capital markets are in supplying funds to new firms by looking at how a platform start-up, ofo, made its investment decisions in response to capital infusions. I fit the business performance of ofo, a bike-sharing platform start-up, in China and show how its financial conditions affected investment decisions. I analyze the effects of funding rounds from venture capitalists on the investment and business of the company with an event study framework. My estimates find that the firm increased its users and bikes by about 40% two weeks before receiving funds, suggesting that it spent much more on bike fleet and promotional offers in expectation of capital infusions. I also show that such boosts in business performance were short-lived: the number of trips and users often returned to normal levels two weeks after the funding day. My findings suggest that the capital market is inefficient in supplying funds to start-up companies. In the third chapter, I study the shakeout in the U.S. automobile industry with data retrieved from old annals of the automobile industry. I simulate a research productivity model and see if I could successfully trigger a shakeout. I find that only the cost reduction from technology advancements is not enough to trigger an industry shakeout and propose that more extreme settings are needed for further studies.

The vehicle manufacturing sector is constantly evolving, and corporations are fully aware of increased consumer expectations for both driver and passenger´s comfort. SCANIA CV AB, as one of the largest Swedish manufacturers of commercial vehicles, has put an emphasis on this area. To guarantee these high-quality standards, several tests are conducted daily. Within this framework, this project aims to gain a better understanding of the phenomena associated with steering wheel vibrations. This project has an experimental focus on recreating sensitive driving conditions and addressing the vibration transfer paths to the main user interface such as the steering wheel. As widely known, the main problems related to vibrations come from resonance excitations. The most obvious solution would be to simply avoid matching any system´s eigenmodes with external excitations. Considering broadband excitations such as bumpy roads or engine vibrations, it is very unlikely that none of the critical frequencies is triggered. A better and more realistic idea would be minimizing the effects of these resonances by structural optimization. However, to do so, the eigenmodes should first be addressed. For this purpose, this project focused on identifying the annoying frequencies triggered while recreating sensitive driving scenarios. These sensitive scenarios were identified by Scania as circumstances in which the steering wheel feel gets altered. Specifically, it was decided to focus on road-induced vibrations, wheel-induced vibrations and engine-induced vibrations. The main findings show that during these tests, some resonances are triggered and interesting features are captured on the steering wheel. / Fordonstillverkningssektorn växer ständigt och företag är fullt medvetna om ökade konsumentförväntningar på både förarens och passagerarnas komfort. SCANIA CV AB, som en av de största svenska tillverkarna av kommersiella fordon, har lagt vikt vid detta område. För att garantera dessa högkvalitativa standarder genomförs flera tester dagligen.Inom denna ram syftar detta projekt till att få en bättre förståelse för de fenomen som är förknippade med rattvibrationer. Detta projekt har ett experimentellt fokus på att återskapa känsliga körförhållanden och adressera vibrationsöverföringsvägarna till huvudanvändargränssnittet, såsom ratten. Som allmänt känt kommer de största problemen relaterade till vibrationer från resonansexcitationer. Den mest uppenbara lösningen skulle vara att helt enkelt undvika att matcha något systems egenmoder med externa excitationer. Med tanke på bredbandsexcitationer som gropiga vägar eller motorvibrationer är det mycket osannolikt att ingen av de kritiska frekvenserna utlöses. En bättre och mer realistisk idé skulle vara att minimera effekterna av dessa resonanser genom strukturell optimering. För att göra det bör egenmoden först behandlas. För detta ändamål fokuserade detta projekt på att identifiera de irriterande frekvenser som triggades samtidigt som känsliga körscenarier återskapades. Dessa känsliga scenarier identifierades av Scania som omständigheter där rattkänslan förändras. Specifikt beslutades att fokusera på väginducerade vibrationer, hjulinducerade vibrationer och motorinducerade vibrationer. Huvudfynden visar att under dessa tester triggas vissa resonanser och intressanta funktioner fångas på ratten.

Private cars are responsible for 15% of carbon emissions in the European Union. Ride hailing services like taxis could serve the door-to-door mobility demand of private car users with fewer overall vehicles. If the service combines multiple user trips, it might even reduce the distance driven compared to private cars which becomes ecologically sustainable. Such ride sharing services are particularly sustainable when many users share one vehicle. But connecting the trips of all users yields many small detours. These detours reduce if some users walk a short distance to a neighboring stop. When multiple stops are combined, vehicles drive to fewer stops. Such stop pooling promises to make ride sharing even more sustainable. Some ride sharing services already integrate short user walks into their system. But the effects of stop pooling on ride sharing systems are yet to be understood. Methods from theoretical physics like mean-field theory and agent-based modeling enable a systemic analysis of complex ride sharing systems. This thesis demonstrates that ride sharing may be more sustainable when users accept short walks. With stop pooling, users wait shorter for vehicles and drive shorter because of more direct vehicle routes. In consequence, the user travel time decreases on average despite additional walk time at constant fleet size. Put differently, stop pooling allows to reduce the fleet size at constant travel time. This also reduces the distance driven by all vehicles that is proportional to the fleet size when sufficient users share one vehicle. This result is robust in a data-driven model using taxi trip data from Manhattan (New York City, USA) with fluctuating demand over the day. At constant fleet size the travel time fluctuates with the demand and might deviate a lot from the expected average travel time. Such unreliable travel times might deter users from ride sharing. However, stop pooling reduces the travel time, the more the higher the travel time without walking. Consequently, stop pooling also reduces the fluctuations in the travel time. This effect is particularly large when adapting the maximum allowed walk distance to the current demand. In adaptive stop pooling users walk further at higher demand. Then, the travel time in ride sharing is more reliable when users accept short walks. All in all, this thesis contributes to the fundamental understanding of the collective dynamics of ride sharing and the effect of stop pooling at a systemic level while also explaining underlying mechanisms. The results suggest that ride sharing providers and users benefit from integrating adaptive stop pooling into the service. Based on the results, a framework can be established that roughly adjusts fleet size to demand to ensure that the ride sharing service operates sustainably. Even if this fleet size remains constant throughout the day, adaptive stop pooling keeps the travel time reliable.:1. Introduction 1 1.1. Private Cars are Unsustainable . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2. Potentially More Sustainable Ride Sharing Faces Detours . . . . . . . . . . . . . 2 1.3. Less Detours in Ride Sharing with Walking to Pooled Stops . . . . . . . . . . . . 4 1.4. Physics Methods Help Understanding Ride Sharing . . . . . . . . . . . . . . . . . 5 1.5. Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2. Fundamentals - A Physics Perspective on Ride Sharing 7 2.1. State of Research on Ride Sharing . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1.1. Ride Sharing Systems are Complex . . . . . . . . . . . . . . . . . . . . . . 8 2.1.2. Measuring Efficiency and Sustainability of Ride Sharing Services . . . . . 8 2.1.3. Ride Sharing might be More Sustainable when Users Accept Short Walks 10 2.1.4. Data-Driven Analysis Yields more Detailed Results . . . . . . . . . . . . . 11 2.1.5. Open Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2. Theoretical Physics Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2.1. What is a Complex System? . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2.2. Mean-Field Theory Simplifies Complex Systems . . . . . . . . . . . . . . 13 2.2.3. Model Complex Systems Based on Agents, not on Equations . . . . . . . 14 2.2.4. Methods from Statistical Physics to Evaluate Multi-Agent Simulations . . 14 2.2.5. Model Street Networks Using Graph Theory . . . . . . . . . . . . . . . . 20 3. Model for Ride Sharing with Walking to Pooled Stops 25 3.1. Ride Sharing Combines Trips with Similar Directions . . . . . . . . . . . . . . . . 25 3.2. Stop Pooling with Dynamic Stop Locations Maintains Flexibility . . . . . . . . . 26 3.3. Simple Algorithm Assigns Users by Reducing Bus Detour . . . . . . . . . . . . . 28 3.3.1. Standard Ride Sharing Algorithm . . . . . . . . . . . . . . . . . . . . . . 28 3.3.2. Stop Pooling Algorithm at Similar Speed . . . . . . . . . . . . . . . . . . 29 3.4. Basic Setting in Continuous Space . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.4.1. Uniform Request Distribution . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.4.2. Heterogeneous Request Distribution . . . . . . . . . . . . . . . . . . . . . 32 3.5. Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.5.1. Relative Distance Driven Measures Ecological Sustainability . . . . . . . . 33 3.5.2. Measure Service Quality by Average User Travel Time . . . . . . . . . . . 34 3.5.3. Further Observables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.5.4. Bisection Method to Find Minimal Travel Time with Small Effort . . . . 36 3.6. Model Extensions Yield More Detailed Results . . . . . . . . . . . . . . . . . . . 37 3.6.1. Fine-Grained Street Network Enables Short Walk Distances . . . . . . . . 38 iii Contents 3.6.2. Data-Driven Demand is Heterogeneous . . . . . . . . . . . . . . . . . . . . 39 3.6.3. Explicit Stop Times Ensure Penalty For Each Stop . . . . . . . . . . . . . 41 3.6.4. Imbalanced Demand Requires Rebalancing of Buses . . . . . . . . . . . . 42 3.6.5. More Detailed Assignment Algorithm Uses Constraints . . . . . . . . . . 43 4. Quantifying Sustainability of Ride Sharing 45 4.1. Two Mechanisms Influence Ride Sharing Sustainability . . . . . . . . . . . . . . . 46 4.1.1. Pickup Detours Increase Distance Driven . . . . . . . . . . . . . . . . . . 46 4.1.2. Trip Overlap Reduces Distance Driven . . . . . . . . . . . . . . . . . . . . 47 4.2. Distance Driven Reduces with Bus Occupancy . . . . . . . . . . . . . . . . . . . 48 4.3. Ride Sharing is more Sustainable than Private Cars for Sufficient Load . . . . . . 50 4.4. Result is Robust for more Complex Models . . . . . . . . . . . . . . . . . . . . . 52 4.5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 5. Ride Sharing Sustainability with Stop Pooling 55 5.1. Ride Sharing Trades Sustainability for Travel Time . . . . . . . . . . . . . . . . . 57 5.2. Stop Pooling is more Sustainable at Same Travel Time . . . . . . . . . . . . . . . 58 5.2.1. Roughly Constant Distance Driven Despite Saved Stops . . . . . . . . . . 58 5.2.2. Stop Pooling Reduces Travel Time . . . . . . . . . . . . . . . . . . . . . . 59 5.2.3. Stop Pooling Breaks The Trade-off Between Sustainability And Travel Time 60 5.3. Higher Stop Pooling Effect for High Loads . . . . . . . . . . . . . . . . . . . . . . 61 5.3.1. Stop Pooling Limits Growth of Best Travel Time . . . . . . . . . . . . . . 62 5.3.2. Stop Pooling Breaks Trade-off for Sufficient Load . . . . . . . . . . . . . . 63 5.4. Robust Effect for Simple Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 5.5. Robust Effect with More Detailed Model . . . . . . . . . . . . . . . . . . . . . . . 66 5.5.1. Load Quantifies Stop Pooling Sustainability . . . . . . . . . . . . . . . . . 67 5.5.2. Already 1.2 Minutes Walk Time might Save 1 Minute Travel Time . . . . 68 5.5.3. Robust Result for Different Parameters . . . . . . . . . . . . . . . . . . . 69 5.6. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 6. Ride Sharing Reliability with Stop Pooling 71 6.1. Unreliable Standard Ride Sharing with Fluctuating Demand . . . . . . . . . . . . 72 6.2. More Reliable Stop Pooling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 6.3. Robust Effect of Stop Pooling with Limited User Delay . . . . . . . . . . . . . . 77 6.4. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 6.5. Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 7. Discussion 81 7.1. Results and Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 7.1.1. When is Ride Sharing More Sustainable than Private Cars? . . . . . . . . 81 7.1.2. How Does Stop Pooling Influence Sustainability of Ride Sharing? . . . . . 82 7.1.3. How Does Stop Pooling Influence Reliability of Ride Sharing? . . . . . . . 82 7.2. Limitations of the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 7.2.1. Simple Algorithms for Ride Sharing and Stop Pooling . . . . . . . . . . . 82 7.2.2. Integrate Adaptive Stop Pooling into Virtual Bus Stops . . . . . . . . . . 83 7.2.3. Distance Driven as Estimator for Ecological Sustainability . . . . . . . . . 83 7.2.4. Deviations from Load Prediction . . . . . . . . . . . . . . . . . . . . . . . 84 7.2.5. Mean-Field Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 7.2.6. Further Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 7.3. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 A. Appendix 87 A.1. Manhattan Street Network Resembles Grid . . . . . . . . . . . . . . . . . . . . . 87 A.2. Computation Details of Bisection Method . . . . . . . . . . . . . . . . . . . . . . 88 A.3. Average Pickup Distance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 A.4. Robustness of Ride Sharing Sustainability . . . . . . . . . . . . . . . . . . . . . . 90 A.5. Stop Pooling Saves Stops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 A.6. Stop Pooling Effectively Reduces Load . . . . . . . . . . . . . . . . . . . . . . . . 92 A.7. Example Breaking of Trade-off in Simple Model . . . . . . . . . . . . . . . . . . . 93 A.8. Transition in Best Walk Limit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 A.9. Maximal Trade-off Shift Increases with Load . . . . . . . . . . . . . . . . . . . . 95 A.10.Rebalancing Buses is more Important with Constraint . . . . . . . . . . . . . . . 97 A.11.Breaking of Trade-off in Complex Model . . . . . . . . . . . . . . . . . . . . . . . 98 A.12.More Stop Pooling at Destinations and High Demand . . . . . . . . . . . . . . . 99 A.13.Roughly Constant Wait and Drive Time in Adaptive Stop Pooling . . . . . . . . 100 A.14.Influence of Capacity Constraint . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 A.15.Walk Time of Rejected Users . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 Bibliography 101 Acknowledgment 116 Statement of Contributions 118

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Tires are the most influential component of the vehicle as they constitute the only contact between the vehicle and the road and have to generate and transmit forces necessary for the driver to control the vehicle. The demand for the tire models are increasing due to the need to study the variations of force generation mechanisms due to various variables such as load, pressure, speed, and road surface irregularities. Another need from the vehicle manufactures is the study of potential incompatibilities associated with safety systems such as Anti-lock Braking System (ABS) and Electronic Stability Control (ESC) and tires. For vehicle dynamic simulations pertaining to the design of safety systems such as ABS, ESC and ride controllers, an accurate and computationally efficient tire model is required. As these control algorithms become more advanced, they require accurate and extended validity in the range of frequencies required to cover dynamic response due to short wavelength road disturbances, braking and steering torque variations. Major thrust has been provided by the tire industry to develop simulation models that accurately predict the dynamic response of tires without the use of computationally intensive tools such as FEA. The objectives of this research are • To develop, implement and validate a rigid ring tire model and a simulation tool to assist both tire designers and the automotive industry in analyzing the effects of tire belt vibrations, road disturbances, and high frequency brake and steering torque variations on the handling, braking, and ride performances of the vehicle. • To further enhance the tire model by considering dynamic stiffness changes and temperature dependent friction properties. • To develop, and implement novel control algorithms for braking, stability, and ride performance improvements of the vehicle / Ph. D.

<p>Minimizing the impact of the transportation sector and across all vehicle classes and sizes on the climate consists one of the main goals globally. As heavy-duty vehicles are responsible for a high share of the total emissions emitted from the transportation sector, multiple initiatives are targeting this vehicle class, with transit buses being no exception. Although battery electric buses (BEBs) have the potential to save energy and decrease emissions, their adoption has been progressing at a slow pace. Despite the advantages of quieter operations, improved acceleration, and absence of diesel or gas odors, there has been limited attention given to the perspective of the users. This study aims to explore the preferences of bus riders towards BEBs and the general public’s behavioral intentions to ride public transit. To achieve these objectives, two separate surveys were designed and disseminated in Salt Lake City, Utah to solicit riders’ and general public’s typical travel behaviors and patterns and their their preferences and opinions regarding the emissions and noise performance of BEBs. The surveys also gauged participants’ attitudes, social norms, and environmental awareness. Statistical analysis revealed that various factors play a role in shaping riders' perceptions regarding the electrification of transit buses. These factors encompass the purpose of the trip, attitudes towards environmental concerns and the environmental effects of battery electric buses (BEBs), as well as non-instrumental ride factors like comfort during the journey and the social image associated with the mode of transportation. Turning to the behavioral intentions toward public transit usage, a structural equation model was estimated that revealed the positive and direct influence of perceived behavioral control, subjective norms, and improved transit ride comfort attributes that BEBs offer. The support for transit bus electrification and green self-identity were found to indirectly influence behavioral intentions. This thesis provides valuable insights into BEBs preferences from the perspectives of riders and general public. Gaining a deeper understanding of the significance of electrification for transit riders, and also, to the general public can enable transit service providers to modify their marketing strategies, promotion strategies, and adapt their overall operations at the system level to accommodate the preferences towards BEBs, and ultimately, attract more transit riders.</p>

As autonomous vehicle (AV) technology evolves and matures, automated public transit (APT) is gaining attention due to its flexibility, cost-effectiveness, and efficiency. This report explores various algorithms for allocating vehicles to passengers within APT systems. It aims to organize and propose effective allocation strategies and validate them through comparative analyses on test networks. Overall, the paper introduces several algorithms, with six specifically compiled and tested using the VIPSim simulator across four traffic networks. Two of these networks are basic, while the other two are more complex and represent real-world scenarios. Through these numerical experiments, the algorithm that maximizes network operational efficiency was identified, and several instructive conclusions were drawn from the comparative analysis.